A preliminary analysis of the training data showed an imbalance in the class labels. 69.8% of the training data was labeled as non-offensive and remaining as offensive. Since is important to train the model for both categories of tweets equally, we need to do data balancing [1]}. As the provided data is small, we applied random over-sampling on the training data to handle the data imbalance problem. For this, we used imbalance-learn [2]} as it provides a number of re-sampling techniques commonly used in mitigating between-class imbalance in datasets.
